Article Style Analyzer
Performs multi-dimensional style analysis on input text and outputs structured JSON results to guide LLMs in mimicking the identified style for new article generation.
Prompt Content
Copy and paste directly into your model or internal evaluation tool.
Please enter the text passage you want to analyze. I will conduct an in-depth style analysis and output the results in a structured format.
Analysis Dimensions
I will analyze text style characteristics from the following dimensions:
- Language Features (syntax, diction, rhetoric)
- Structural Features (paragraphs, transitions, hierarchy)
- Narrative Features (perspective, distance, chronology)
- Emotional Features (intensity, expression, tone)
- Thinking Features (logic, depth, rhythm)
- Unique Markers (distinctive expressions, imagery systems)
- Cultural Depth (allusions, knowledge domains)
- Rhythm & Prosody (syllables, pauses, tempo)
Output Format
I will output the analysis results in the following structured format within a code block:
{ "style_summary": "One-sentence style summary", "language": { "sentence_pattern": ["Primary sentence pattern", "Secondary pattern"], "word_choice": { "formality_level": "Formality level 1-5", "preferred_words": ["High-frequency word 1", "Feature word 2"], "avoided_words": ["Avoided word type 1", "Avoided type 2"] }, "rhetoric": ["Primary rhetorical device 1", "Device 2"] }, "structure": { "paragraph_length": "Average paragraph word count", "transition_style": "Transition characteristics", "hierarchy_pattern": "Hierarchical development method" }, "narrative": { "perspective": "Narrative perspective", "time_sequence": "Temporal treatment", "narrator_attitude": "Narrator's attitude" }, "emotion": { "intensity": "Emotional intensity 1-5", "expression_style": "Expression method", "tone": "Emotional tone" }, "thinking": { "logic_pattern": "Thought progression method", "depth": "Thinking depth 1-5", "rhythm": "Cognitive rhythm feature" }, "uniqueness": { "signature_phrases": ["Signature expression 1", "Expression 2"], "imagery_system": ["Core image 1", "Image 2"] }, "cultural": { "allusions": ["Allusion type", "Usage frequency"], "knowledge_domains": ["Domain 1", "Domain 2"] }, "rhythm": { "syllable_pattern": "Syllable characteristics", "pause_pattern": "Pause pattern", "tempo": "Tempo feature" } }
Notes
Do not extract specific elements such as book titles, author names, or specific geographic locations. The purpose of style extraction is to generate new articles on specified topics based on this style. Extraction elements should be based on this task.
Use Cases
Reference Output
```json { "style_summary": "Rational and rigorous, logically clear, adept at using parallelism and rhetorical questions, language is formal and academically profound.", "language": { "sentence_pattern": ["Predominantly complex long sentences", "Rhetorical questions as lead-ins"], "word_choice": { "formality_level": "4", "preferred_words": ["mechanism", "paradigm", "structural"], "avoided_words": ["colloquial expressions", "internet slang"] }, "rhetoric": ["Parallelism", "Rhetorical questions"] }, "structure": { "paragraph_length": "150-200 words", "transition_style": "Logical progression", "hierarchy_pattern": "General-specific-general structure" }, "narrative": { "perspective": "Third-person omniscient", "time_sequence": "Linear chronology", "narrator_attitude": "Objective and neutral" }, "emotion": { "intensity": "2", "expression_style": "Reserved and restrained", "tone": "Calm and rational" }, "thinking": { "logic_pattern": "Combination of induction and deduction", "depth": "4", "rhythm": "Steady progression" }, "uniqueness": { "signature_phrases": ["It can be seen that", "Further analysis shows"], "imagery_system": ["Architectural metaphor", "System metaphor"] }, "cultural": { "allusions": ["Academic allusions", "Low frequency usage"], "knowledge_domains": ["Social sciences", "Philosophy"] }, "rhythm": { "syllable_pattern": "Predominantly multi-syllable words", "pause_pattern": "Regular punctuation pauses", "tempo": "Moderate and steady" } } ```
Scoring Rubric
Excellent: Complete analysis dimensions, accurate feature extraction, standardized JSON structure, no information omissions. Good: Covers main dimensions, basic feature descriptions are accurate, structure is complete. Satisfactory: Some dimensions missing or descriptions vague, but core style characteristics are reflected. Poor: Incorrect output format, missing key information, or includes specific entity information that should not be extracted.
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